In RamiKrispin/covid19sf: The Covid19 San Francisco Dataset
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
`%>%` <- magrittr::`%>%`
covid19sf
The covid19sf package provides a daily summary of the covid19 cases in San Francisco. The package includes the following datasets:
covid19sf_geo - Confirmed cases and deaths summarized by geographycovid19sf_hospital - Hospital capacity datacovid19sf_hospitalizations - Hospitalizations datacovid19sf_housing - Alternative housing sitescovid19sf_test_loc - Testing locationscovid19sf_tests - Daily number of testscovid19sf_vaccine_demo - Summary of vaccine doses given to San Franciscans by demographics groups (age and race)covid19sf_vaccine_demo_ts - Time series view of vaccine doses given to San Franciscans by demographics groups (age and race)covid19sf_vaccine_geo - COVID-19 vaccines given to San Franciscans by geographycovid19sf_population - COVID-19 cases by population characteristics over time
The following dataset were deprecated and replaced by the covid19sf_population dataset:
covid19sf_demo- Cases summarized by date, transmission and case dispositioncovid19sf_homeless - Confirmed cases by homelessnesscovid19sf_age - Cases summarized by age groupcovid19sf_gender - Confirmed cases summarized by gendercovid19sf_summary - Cases summarized by date, transmission and case disposition
Data soucre: San Francisco, Department of Public Health - Population Health Division through the San Francisco Opne Data protal website
Installation
# install.packages("devtools")
devtools::install_github("RamiKrispin/covid19sf")
Usage
The ccovid19sf package provides different views for the covid19 cases in San Francisco. That includes case distribution by age, gender, race, etc. The following examples demonstrate some of the data use cases.
library(covid19sf)
Cases distribution by demographic
The covid19sf_population provides a daily summary of new and cumulative positive cases by the following demograpich groups:
- Age group
- Comorbidities
- Gender
- Homelessness
- Race/Ethnicity
- Sexual Orientation
- Single Room Occupancy Tenancy
- Skilled Nursing Facility Occupancy
- Transmission Type
data(covid19sf_population)
head(covid19sf_population)
Cases distribution by age
To get cases view by age group we will use the characteristic_type variable to filter the data:
library(dplyr)
df_age <- covid19sf_population %>%
filter(characteristic_type == "Age Group")
head(df_age)
Ordering the age groups before plotting the cases distribution:
age_order <- df_age %>%
select(characteristic_group, characteristic_group_sort_order) %>%
distinct() %>%
arrange(characteristic_group_sort_order)
df_age$characteristic_group <- factor(df_age$characteristic_group, levels = age_order$characteristic_group)
The following box-plot shows the distribution of the positive cases by age group:
library(plotly)
plot_ly(df_age,
color = ~ characteristic_group,
y = ~ new_cases,
boxpoints = "all",
jitter = 0.3,
pointpos = -1.8,
type = "box" ) %>%
layout(title = "Distribution of Daily New COVID-19 Cases in San Francisco by Age Group",
yaxis = list(title = "Number of Cases"),
xaxis = list(title = "Source: San Francisco Department of Public Health"),
legend = list(x = 0.9, y = 0.9),
margin = list(t = 60, b = 60, l = 60, r = 60))
library(plotly)
p1 <- plot_ly(df_age,
color = ~ characteristic_group,
y = ~ new_cases,
boxpoints = "all",
jitter = 0.3,
pointpos = -1.8,
type = "box" ) %>%
layout(title = "Distribution of Daily New COVID-19 Cases in San Francisco by Age Group",
yaxis = list(title = "Number of Cases"),
xaxis = list(title = "Source: San Francisco Department of Public Health"),
legend = list(x = 0.9, y = 0.9),
margin = list(t = 60, b = 60, l = 60, r = 60))
orca(p1, "man/figures/age_dist1.svg", width = 8 * 96, height = 5 * 96)
Here is the overall distribution of cases by age group as of r max(df_age$specimen_collection_date):
df_age %>%
filter(specimen_collection_date == max(specimen_collection_date)) %>%
plot_ly(values = ~ cumulative_cases,
labels = ~ characteristic_group,
type = "pie",
textposition = 'inside',
textinfo = 'label+percent',
insidetextfont = list(color = '#FFFFFF'),
hoverinfo = 'text',
text = ~paste(" Age Group:", characteristic_group, "<br>",
"Total:", cumulative_cases, "<br>",
"Population Estimation:", population_estimate,
paste("(",round(100* cumulative_cases/population_estimate, 1) ,"%)", sep = ""))) %>%
layout(title = ~ paste("Total Cases Dist. by Age Group as of", max(specimen_collection_date)),
margin = list(t = 60, b = 20, l = 30, r = 60))
library(dplyr)
library(plotly)
p <- df_age %>%
filter(specimen_collection_date == max(specimen_collection_date)) %>%
plot_ly(values = ~ cumulative_cases,
labels = ~ characteristic_group,
type = "pie",
textposition = 'inside',
textinfo = 'label+percent',
insidetextfont = list(color = '#FFFFFF'),
hoverinfo = 'text',
text = ~paste(" Age Group:", characteristic_group, "<br>",
"Total:", cumulative_cases, "<br>",
"Population Estimation:", population_estimate,
paste("(",round(100* cumulative_cases/population_estimate, 1) ,"%)", sep = ""))) %>%
layout(title = ~ paste("Total Cases Dist. by Age Group as of", max(specimen_collection_date)),
margin = list(t = 60, b = 20, l = 30, r = 60))
orca(p, "man/figures/age_dist2.svg", width = 5 * 96, height = 5 * 96)
Geospatial visualiztion
The package provides several geo-spatial dataset:
covid19sf_vaccine_geo - COVID-19 vaccines given to San Franciscans by geographycovid19sf_geo - Confirmed cases and deaths summarized by geographycovid19sf_test_loc - Testing locations
Those three datasets are sf objects, ready to use. For example, plotting the COVID19 vaccination data by geography:
library(sf)
data(covid19sf_vaccine_geo)
str(covid19sf_vaccine_geo)
df <- covid19sf_vaccine_geo %>% filter(area_type == "Analysis Neighborhood") %>%
dplyr::mutate(perc_complated = percent_pop_series_completed * 100)
We will plot the object Using the sf package:
plot(df[, c("perc_complated", "geometry")],
main = "San Francisco - Percentage of Fully Vaccinated Population by Geo",
key.pos = 1, axes = TRUE, key.width = lcm(1.2), key.length = 1.0)
More examples available on this vignette.
Tests results distribution
The covid19sf_tests provides a daily summary of the daily number of tests and their results (positive, negative, and indeterminate):
data(covid19sf_tests)
head(covid19sf_tests)
The plot below shows the daily distribution of the results of the tests:
covid19sf_tests %>%
plotly::plot_ly(x = ~ specimen_collection_date,
y = ~ pos,
name = "Positive",
type = 'scatter',
mode = 'none',
stackgroup = 'one',
fillcolor = "red") %>%
plotly::add_trace(y = ~ neg, name = "Negative", fillcolor = "green") %>%
plotly::add_trace(y = ~ indeterminate, name = "Indeterminate", fillcolor = "gray") %>%
plotly::layout(title = "Tests Results Distribution",
yaxis = list(title = "Tests Count"),
xaxis = list(title = "Source: San Francisco Department of Public Health"),
legend = list(x = 0.1, y = 0.9))
p <- covid19sf_tests %>%
plotly::plot_ly(x = ~ specimen_collection_date,
y = ~ pos,
name = "Positive",
type = 'scatter',
mode = 'none',
stackgroup = 'one',
fillcolor = "red") %>%
plotly::add_trace(y = ~ neg, name = "Negative", fillcolor = "green") %>%
plotly::add_trace(y = ~ indeterminate, name = "Indeterminate", fillcolor = "gray") %>%
plotly::layout(title = "Tests Results Distribution",
yaxis = list(title = "Tests Count"),
xaxis = list(title = "Source: San Francisco Department of Public Health"),
legend = list(x = 0.1, y = 0.9))
orca(p, "man/figures/test_dist.svg", width = 8 * 96, height = 5 * 96)
Cases distribution by race ethnicity
The covid19sf_population dataset provides a daily summary of the COVID19 positive cases by race and ethnicity:
data(covid19sf_population)
head(covid19sf_population)
Below is a plot of the cumulative positive cases by race and ethnicity:
covid19sf_population %>%
filter(characteristic_type == "Race/Ethnicity") %>%
dplyr::arrange(specimen_collection_date) %>%
plotly::plot_ly(x = ~ specimen_collection_date,
y = ~ cumulative_cases,
# name = 'Cases',
type = 'scatter',
mode = 'none',
color = ~characteristic_group,
stackgroup = 'one') %>%
layout(title = "Total Cases Dist. by Race and Ethnicity",
legend = list(x = 0.05, y = 0.9),
yaxis = list(title = "Number of Cases", tickformat = ".0f"),
xaxis = list(title = "Source: San Francisco Department of Public Health"))
p <- covid19sf_population %>%
filter(characteristic_type == "Race/Ethnicity") %>%
dplyr::arrange(specimen_collection_date) %>%
plotly::plot_ly(x = ~ specimen_collection_date,
y = ~ cumulative_cases,
# name = 'Cases',
type = 'scatter',
mode = 'none',
color = ~characteristic_group,
stackgroup = 'one') %>%
layout(title = "Total Cases Dist. by Race and Ethnicity",
legend = list(x = 0.05, y = 0.9),
yaxis = list(title = "Number of Cases", tickformat = ".0f"),
xaxis = list(title = "Source: San Francisco Department of Public Health"))
orca(p, "man/figures/demo_dist.svg", width = 8 * 96, height = 5 * 96)
RamiKrispin/covid19sf documentation built on April 3, 2023, 4:11 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" ) `%>%` <- magrittr::`%>%`
covid19sf
The covid19sf package provides a daily summary of the covid19 cases in San Francisco. The package includes the following datasets:
covid19sf_geo- Confirmed cases and deaths summarized by geographycovid19sf_hospital- Hospital capacity datacovid19sf_hospitalizations- Hospitalizations datacovid19sf_housing- Alternative housing sitescovid19sf_test_loc- Testing locationscovid19sf_tests- Daily number of testscovid19sf_vaccine_demo- Summary of vaccine doses given to San Franciscans by demographics groups (age and race)covid19sf_vaccine_demo_ts- Time series view of vaccine doses given to San Franciscans by demographics groups (age and race)covid19sf_vaccine_geo- COVID-19 vaccines given to San Franciscans by geographycovid19sf_population- COVID-19 cases by population characteristics over time
The following dataset were deprecated and replaced by the covid19sf_population dataset:
covid19sf_demo- Cases summarized by date, transmission and case dispositioncovid19sf_homeless- Confirmed cases by homelessnesscovid19sf_age- Cases summarized by age groupcovid19sf_gender- Confirmed cases summarized by gendercovid19sf_summary- Cases summarized by date, transmission and case disposition
Data soucre: San Francisco, Department of Public Health - Population Health Division through the San Francisco Opne Data protal website
Installation
# install.packages("devtools") devtools::install_github("RamiKrispin/covid19sf")
Usage
The ccovid19sf package provides different views for the covid19 cases in San Francisco. That includes case distribution by age, gender, race, etc. The following examples demonstrate some of the data use cases.
library(covid19sf)
Cases distribution by demographic
The covid19sf_population provides a daily summary of new and cumulative positive cases by the following demograpich groups:
- Age group
- Comorbidities
- Gender
- Homelessness
- Race/Ethnicity
- Sexual Orientation
- Single Room Occupancy Tenancy
- Skilled Nursing Facility Occupancy
- Transmission Type
data(covid19sf_population) head(covid19sf_population)
Cases distribution by age
To get cases view by age group we will use the characteristic_type variable to filter the data:
library(dplyr) df_age <- covid19sf_population %>% filter(characteristic_type == "Age Group") head(df_age)
Ordering the age groups before plotting the cases distribution:
age_order <- df_age %>% select(characteristic_group, characteristic_group_sort_order) %>% distinct() %>% arrange(characteristic_group_sort_order) df_age$characteristic_group <- factor(df_age$characteristic_group, levels = age_order$characteristic_group)
The following box-plot shows the distribution of the positive cases by age group:
library(plotly) plot_ly(df_age, color = ~ characteristic_group, y = ~ new_cases, boxpoints = "all", jitter = 0.3, pointpos = -1.8, type = "box" ) %>% layout(title = "Distribution of Daily New COVID-19 Cases in San Francisco by Age Group", yaxis = list(title = "Number of Cases"), xaxis = list(title = "Source: San Francisco Department of Public Health"), legend = list(x = 0.9, y = 0.9), margin = list(t = 60, b = 60, l = 60, r = 60))
library(plotly) p1 <- plot_ly(df_age, color = ~ characteristic_group, y = ~ new_cases, boxpoints = "all", jitter = 0.3, pointpos = -1.8, type = "box" ) %>% layout(title = "Distribution of Daily New COVID-19 Cases in San Francisco by Age Group", yaxis = list(title = "Number of Cases"), xaxis = list(title = "Source: San Francisco Department of Public Health"), legend = list(x = 0.9, y = 0.9), margin = list(t = 60, b = 60, l = 60, r = 60)) orca(p1, "man/figures/age_dist1.svg", width = 8 * 96, height = 5 * 96)
Here is the overall distribution of cases by age group as of r max(df_age$specimen_collection_date):
df_age %>% filter(specimen_collection_date == max(specimen_collection_date)) %>% plot_ly(values = ~ cumulative_cases, labels = ~ characteristic_group, type = "pie", textposition = 'inside', textinfo = 'label+percent', insidetextfont = list(color = '#FFFFFF'), hoverinfo = 'text', text = ~paste(" Age Group:", characteristic_group, "<br>", "Total:", cumulative_cases, "<br>", "Population Estimation:", population_estimate, paste("(",round(100* cumulative_cases/population_estimate, 1) ,"%)", sep = ""))) %>% layout(title = ~ paste("Total Cases Dist. by Age Group as of", max(specimen_collection_date)), margin = list(t = 60, b = 20, l = 30, r = 60))
library(dplyr) library(plotly) p <- df_age %>% filter(specimen_collection_date == max(specimen_collection_date)) %>% plot_ly(values = ~ cumulative_cases, labels = ~ characteristic_group, type = "pie", textposition = 'inside', textinfo = 'label+percent', insidetextfont = list(color = '#FFFFFF'), hoverinfo = 'text', text = ~paste(" Age Group:", characteristic_group, "<br>", "Total:", cumulative_cases, "<br>", "Population Estimation:", population_estimate, paste("(",round(100* cumulative_cases/population_estimate, 1) ,"%)", sep = ""))) %>% layout(title = ~ paste("Total Cases Dist. by Age Group as of", max(specimen_collection_date)), margin = list(t = 60, b = 20, l = 30, r = 60)) orca(p, "man/figures/age_dist2.svg", width = 5 * 96, height = 5 * 96)
Geospatial visualiztion
The package provides several geo-spatial dataset:
covid19sf_vaccine_geo- COVID-19 vaccines given to San Franciscans by geographycovid19sf_geo- Confirmed cases and deaths summarized by geographycovid19sf_test_loc- Testing locations
Those three datasets are sf objects, ready to use. For example, plotting the COVID19 vaccination data by geography:
library(sf) data(covid19sf_vaccine_geo) str(covid19sf_vaccine_geo) df <- covid19sf_vaccine_geo %>% filter(area_type == "Analysis Neighborhood") %>% dplyr::mutate(perc_complated = percent_pop_series_completed * 100)
We will plot the object Using the sf package:
plot(df[, c("perc_complated", "geometry")], main = "San Francisco - Percentage of Fully Vaccinated Population by Geo", key.pos = 1, axes = TRUE, key.width = lcm(1.2), key.length = 1.0)
More examples available on this vignette.
Tests results distribution
The covid19sf_tests provides a daily summary of the daily number of tests and their results (positive, negative, and indeterminate):
data(covid19sf_tests) head(covid19sf_tests)
The plot below shows the daily distribution of the results of the tests:
covid19sf_tests %>% plotly::plot_ly(x = ~ specimen_collection_date, y = ~ pos, name = "Positive", type = 'scatter', mode = 'none', stackgroup = 'one', fillcolor = "red") %>% plotly::add_trace(y = ~ neg, name = "Negative", fillcolor = "green") %>% plotly::add_trace(y = ~ indeterminate, name = "Indeterminate", fillcolor = "gray") %>% plotly::layout(title = "Tests Results Distribution", yaxis = list(title = "Tests Count"), xaxis = list(title = "Source: San Francisco Department of Public Health"), legend = list(x = 0.1, y = 0.9))
p <- covid19sf_tests %>% plotly::plot_ly(x = ~ specimen_collection_date, y = ~ pos, name = "Positive", type = 'scatter', mode = 'none', stackgroup = 'one', fillcolor = "red") %>% plotly::add_trace(y = ~ neg, name = "Negative", fillcolor = "green") %>% plotly::add_trace(y = ~ indeterminate, name = "Indeterminate", fillcolor = "gray") %>% plotly::layout(title = "Tests Results Distribution", yaxis = list(title = "Tests Count"), xaxis = list(title = "Source: San Francisco Department of Public Health"), legend = list(x = 0.1, y = 0.9)) orca(p, "man/figures/test_dist.svg", width = 8 * 96, height = 5 * 96)
Cases distribution by race ethnicity
The covid19sf_population dataset provides a daily summary of the COVID19 positive cases by race and ethnicity:
data(covid19sf_population) head(covid19sf_population)
Below is a plot of the cumulative positive cases by race and ethnicity:
covid19sf_population %>% filter(characteristic_type == "Race/Ethnicity") %>% dplyr::arrange(specimen_collection_date) %>% plotly::plot_ly(x = ~ specimen_collection_date, y = ~ cumulative_cases, # name = 'Cases', type = 'scatter', mode = 'none', color = ~characteristic_group, stackgroup = 'one') %>% layout(title = "Total Cases Dist. by Race and Ethnicity", legend = list(x = 0.05, y = 0.9), yaxis = list(title = "Number of Cases", tickformat = ".0f"), xaxis = list(title = "Source: San Francisco Department of Public Health"))
p <- covid19sf_population %>% filter(characteristic_type == "Race/Ethnicity") %>% dplyr::arrange(specimen_collection_date) %>% plotly::plot_ly(x = ~ specimen_collection_date, y = ~ cumulative_cases, # name = 'Cases', type = 'scatter', mode = 'none', color = ~characteristic_group, stackgroup = 'one') %>% layout(title = "Total Cases Dist. by Race and Ethnicity", legend = list(x = 0.05, y = 0.9), yaxis = list(title = "Number of Cases", tickformat = ".0f"), xaxis = list(title = "Source: San Francisco Department of Public Health")) orca(p, "man/figures/demo_dist.svg", width = 8 * 96, height = 5 * 96)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.